The ‘ — to seamlessly identify the participants + 9 ideas for and start_ups from my personal backlog_ Smart Handshake’ apps 1. The ‘Smart Handshake’ Did you ever have difficulties remembering names after a business or social handshake? If yes, check this seamless solution! image: pixabay : in a business environment and, with no further interaction or activity, both persons receive a notification about each other, with name, photo, location, context and link to his/her LinkedIn or other social profile! Imagine this scenario Two persons handshake An app using the accelerometer, gyroscope and motion detection capabilities to identify the ‘universal’ pattern of a handshake A business handshake between two persons who have installed the in their smartphone or wearable, will trigger the following sequence of events: app : based on patterns extracted from a vast volume of handshake events — movement data, the system is able to identify the based on acceleration, speed, duration and the movement of the hand. The app uses the accelerometer and related tech to identify the ‘universal’ movement of hands during a typical business handshake gesture When the with confidence, it is logged in the data store — independently for each of the users. The handshake event also contains the timestamp and location information. handshake is identified The system searches for ; . other handshakes happened at the same time and location the closest one in time and location refers to the other person of the handshake event . The app can lookup their social network profile links — for instance, LinkedIn or Facebook. Each user receives information about the other’s person identity and social media profile. At this point both persons involved in the handshake, are identified As users continue to meet people, of the new persons met (names, photos, social network profiles) and — under sufficient permissions — can automatically invite or follow the other user on the default social network. the app maintains the history 2. DIGITAL annotations on PHYSICAL books You are reading a book — a physical one; it might be a novel, a technical or text book. You are at an important point/ paragraph where you need help or you need to comment on/ with a question or explanation. image: pixabay : Imagine the following scenario You use your smartphone to scan the paragraph or phrase of interest — the physical book from the paragraph/phrase/ page you just scan The app performs OCR to extract the text against a large database of books — could be a service call such as or similar. The app triggers a full-text-search Google’s Book API The app receives the response from the API — including the — a reference to the paragraph and page. identifier of the book and the positioning the specific paragraph/phrase/page of the identified book. The app retrieves user-generated-content and metadata about The summarized is then presented to the user via the app — possibly in an and/ or with voice support. user generated content Augmented Reality mode The user can use , via the app, to append his/her own private or public comments on the identified paragraph of the book. voice Full history is maintained for the user and the book — available also via classic search experience. 3. A self-organizing ‘Do Not Disturb’ mode Ever been in theater, cinema or other noise-sensitive social situation where sounds from mobile notifications can spoil the moment? The common sense in such a situation is to set the mobile in silent or mode. Although obvious, this is not the case for everybody: there are always those few who either by mistake or disrespectfully skip this. ‘Do not disturb’ What if there was a way for the audience to seamlessly self-organize? ‘The system’ could identify the situation as requiring ‘silent mode’ and notify the members of the audience to silent their mobiles (those who haven’t already); Or, in a more aggressive scenario, automatically set the phones in to ‘Do not Disturb’ mode Mobile devices automatically enter silent mode when users join special social arrangements (a concert, a lecture etc.). with no controlling system or particular rules: Assuming a number of people is at a particular place — within a specific radius and possibly around a particular known location; each time a mobile device is set to ‘silent mode’ by a user, an event is triggered which sends location and mode data into a centralized data store; this database allows the identification of ‘concurrent’ transitions to ‘silent mode’ within the same radius. This could happen seamlessly Multiple human-originated transitions to ‘silent mode’ which are time-aligned and within the same radius, indicate a self-adjusting behavior (people set their mobile phones to ‘silent mode’ at the same time and possibly for the same reason) If this behavior is significant (as a percentage of the audience — more than x% of the people identified in the same radius and time frame) there is (people arrangement+point in time+ location) is requiring mobile devices in silent mode. Assuming that this behavior follows particular patterns — like specific days of the week, months, time-slots within the day, size of audience, time-frame length etc. — the system can safely identify this location and time arrangement as ‘sensitive to noise’. a clear signal that the particular situation Read more here +7 ideas on music, news, messaging and more — follow the or click on the image: link 7 High-potential startup concepts Images: pixabay

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